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迁移框选数据从config读取

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zqc
2026-02-27 14:39:43 +08:00
parent 16a55fadd7
commit 5670b702f9
1 changed files with 87 additions and 66 deletions
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47
biz/prison/trajectory02_biz.py
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@@ -25,6 +25,9 @@ from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主
from yolox.tracker.byte_tracker import BYTETracker from yolox.tracker.byte_tracker import BYTETracker
from utils.logger import get_logger
logger = get_logger(__name__)
# ========================= 配置区 ========================= # ========================= 配置区 =========================
# Kadian 模型路径与ROI（可根据实际情况修改） # Kadian 模型路径与ROI（可根据实际情况修改）
detector_model_path = 'YOLO_Weight/prisoner_model.onnx' detector_model_path = 'YOLO_Weight/prisoner_model.onnx'
@@ -38,9 +41,15 @@ RTSP_TARGET_FPS = 10.0
# 新增：告警推送频率限制（秒） # 新增：告警推送频率限制（秒）
ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次
# 默认 ROI 相对坐标（区域多边形）
DEFAULT_ROI_RELATIVE = [(0.47, 0.35), (0.5, 0.35), (0.7, 1.0), (0.3, 1.0)]
class TrajectoryDetector: class TrajectoryDetector:
def __init__(self): def __init__(self, params=None):
# 摄像头额外参数
self.params = params if params is not None else {}
# 模型加载 # 模型加载
self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45,
input_size=input_size) input_size=input_size)
@@ -75,12 +84,12 @@ class TrajectoryDetector:
self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps)
self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps)
print(f"\n超参数设置:") logger.debug(f"\n超参数设置:")
print(f" FPS: {self.fps:.2f}") logger.debug(f" FPS: {self.fps:.2f}")
print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") logger.debug(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧")
print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") logger.debug(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧")
print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") logger.debug(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧")
print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") logger.debug(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧")
# ========================================== # ==========================================
# 状态变量初始化 # 状态变量初始化
@@ -102,10 +111,13 @@ class TrajectoryDetector:
# ========================= # =========================
# ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1 # ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1
# 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208 # 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208
# ROI 处理：优先从 params 获取，否则使用默认值 DEFAULT_ROI_RELATIVE
roi_points = self.params.get('roi_points', DEFAULT_ROI_RELATIVE)
self.route_rois = [ self.route_rois = [
{ {
"name": "zone", # 单一区域，犯人离开即报警 "name": "zone", # 单一区域，犯人离开即报警
"polygon_rel": [(0.47, 0.35), (0.5, 0.35), (0.7, 1.0), (0.3, 1.0)] # 相对坐标，可自定义 "polygon_rel": roi_points # 相对坐标，从params获取或使用默认值
} }
] ]
@@ -113,7 +125,7 @@ class TrajectoryDetector:
self.width = 0 self.width = 0
self.height = 0 self.height = 0
print(f"相对坐标 ROI: {self.route_rois}") logger.debug(f"相对坐标 ROI: {self.route_rois}")
# 每个犯人（track_id）一套状态 # 每个犯人（track_id）一套状态
self.prisoner_route_state = {} self.prisoner_route_state = {}
@@ -498,19 +510,22 @@ class FrameProcessorWorker(threading.Thread):
except queue.Empty: except queue.Empty:
continue continue
try:
cam_id = item["camera_id"] cam_id = item["camera_id"]
ts = item["timestamp"] ts = item["timestamp"]
frame = item["frame"] frame = item["frame"]
# 抽帧控制 # 抽帧控制
if ts - self.last_ts.get(cam_id, 0) < target_interval: if ts - self.last_ts.get(cam_id, 0) < target_interval:
self.raw_queue.task_done() # self.raw_queue.task_done()
continue continue
self.last_ts[cam_id] = ts self.last_ts[cam_id] = ts
# 获取检测器实例 # 获取检测器实例
if cam_id not in self.trajectory_detectors: if cam_id not in self.trajectory_detectors:
self.trajectory_detectors[cam_id] = TrajectoryDetector() camera_config = self.cameras.get(cam_id)
params = camera_config.params if camera_config else None
self.trajectory_detectors[cam_id] = TrajectoryDetector(params)
detector = self.trajectory_detectors[cam_id] detector = self.trajectory_detectors[cam_id]
# 执行检测 # 执行检测
@@ -540,7 +555,7 @@ class FrameProcessorWorker(threading.Thread):
try: try:
img_b64 = self._encode_image_to_base64(result_img) img_b64 = self._encode_image_to_base64(result_img)
except Exception as e: except Exception as e:
print(f"[ERROR] Encode image failed: {e}") logger.error(f"[ERROR] Encode image failed: {e}")
img_b64 = None img_b64 = None
if img_b64 is not None: if img_b64 is not None:
@@ -556,6 +571,12 @@ class FrameProcessorWorker(threading.Thread):
# if push_actions and len(push_actions) > 0: # if push_actions and len(push_actions) > 0:
# self.ws_queue_2.put(msg, timeout=1.0) # self.ws_queue_2.put(msg, timeout=1.0)
except queue.Full: except queue.Full:
print("[WARN] ws_send_queue full, drop frame message") logger.warning("[WARN] ws_send_queue full, drop frame message")
except Exception as e:
logger.error(
f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}")
logger.exception("Exception details:") # 打印完整的堆栈跟踪
# 继续处理下一帧，不要退出循环
finally:
self.raw_queue.task_done() self.raw_queue.task_done()